A robust predictive current control for PMSM based on extended state observer

Chen, Hongjun; Qu, Jialong; Liu, Bo; Xu, Haoyang

doi:10.1109/cyber.2015.7288202

Cited by 12 publications

(3 citation statements)

References 15 publications

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“…Papers [19] and [20] use current errors to estimate the real-time flux so as to remove the d-axis current static errors, but integrators have to be adopted, lowering the system bandwidth and dynamics. In addition, another interesting solution to the parameter mismatch issue is to construct the perturbation observers that can be integrated into the prediction plant model [21][22][23]. This method is capable of detecting the general disturbances caused not only by parameter uncertainties but also the system nonlinearities and even external disturbances.…”

Section: Introductionmentioning

confidence: 99%

“…This method is capable of detecting the general disturbances caused not only by parameter uncertainties but also the system nonlinearities and even external disturbances. Paper [21] proposes a disturbance observer based on feedback compensation to solve the inductance and bus voltage variation problem, and a discrete extended state observer is constructed in [22] to detect the current and disturbances in conditions of parameter variations and improve the robustness of the MPC controller. A discrete Luenberger observer is designed to estimate the future values of stator current and the disturbance for induction machines in [23], but this method has only been used in the modulator-retained MPCC systems, which needs to be deeply investigated in the FCS-MPCC algorithms for PMSM applications.…”

Section: Introductionmentioning

confidence: 99%

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Robust Perturbation Observer-based Finite Control Set Model Predictive Current Control for SPMSM Considering Parameter Mismatch

Liu

Zhao

2019

Energies

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In order to improve the dynamics of the surface-mounted permanent magnet synchronous motors (SPMSM) used in servo systems, finite control set model predictive current control (FCS-MPCC) methods have been widely adopted. However, because the FCS-MPCC is a model-based strategy, its performance highly depends on the machine parameters, such as the winding resistance, inductance and flux linkage. Unfortunately, the parameter mismatch problem is common due to the measurement precision and environmental impacts (e.g., temperature). To enhance the robustness of the SPMSM FCS-MPCC systems, this paper proposes a Lundberg perturbation observer that is seldom used in the FCS model predictive control situations to remove the adverse effects caused by resistance and inductance mismatch. Firstly, the system model is established, and the FCS-MPCC mechanism is illustrated. Based on the machine model, the sensitivity of the control algorithm to the parameter mismatch is discussed. Then, the Luenberger perturbation observer that can estimate the general disturbance arising from the parameter uncertainties is developed, and the stability of the observer is analyzed by using the discrete pole assignment technique. Finally, the proposed disturbance observer is incorporated into the FCS-MPCC prediction plant model for real-time compensation. Both simulation and experiments are conducted on a three-phase SPMSM, verifying that the proposed strategy has marked control performance and strong robustness.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Perturbation Observer-based Finite Control Set Model Predictive Current Control for SPMSM Considering Parameter Mismatch

Liu

Zhao

2019

Energies

View full text Add to dashboard Cite

show abstract

“…Despite its great potential, the biggest challenge in MPC implementation is the non‐linear behaviour of the drive, since the full and precise knowledge of the parameters is needed for the prediction and for the disturbance rejection, in order to avoid signal oscillations and steady‐state error. To overcome this limit, in [10], an extended state observer (ESO) is used to improve the steady‐state performance of the system. Also in [11], a predictive control is enhanced with an observer based on internal model, which exploits a simple updating law for fast disturbance estimation, influencing also the distorted frequency behaviour of the system output.…”

Section: Introductionmentioning

confidence: 99%